Everyone now seems to recognise the potential of data-driven marketing, yet the quality and actual value of data itself is still extremely difficult to determine. Not surprisingly, more and more industry leaders have started concerning themselves with data quality.

Nowadays we see articles on green data, clean data, data accuracy, responsible data, self-reported data and data actuality, but it’s important to consider all these aspects together, rather than in silos, to be able to truly establish data quality.

To achieve this accuracy at scale, here are five key principles to consider:

1.Observed vs self-reported data

As human beings we tend to ‘project a somewhat rosier image of ourselves to the outside world’. Take social media for example - is anyone really who they pretend to be online?

Clearly, we can’t always be relied upon to provide the most accurate account of ourselves, and this is equally relevant to the commercial sphere. Nevertheless, a remarkable number of data solutions today still rely on users to submit their preferences, habits, likes, and dislikes first hand, or glean it from consumer surveys and social ‘likes’. Facebook ‘likes’ for example, seem like the driving passion for many social media marketers. They somehow think that more Facebook likes is the panacea for all the world’s ills. But are they really that important? Maybe. But maybe not. We need to look at the data in order to find out if these things (likes, dislikes, habits…) collected from whatever platform are all they’re cracked up to be.

That’s why the most reliable tools on the market today don’t use self-reported but observed data; collected from genuine user behaviour that allows for accuracy at scale for both brands and consumers. Marketers that target based on what consumers actually do, search for or use will find themselves at a significant advantage over those still relying on self-reporting.

2.Mapping the complete mobile user journey

By now, most marketers will be on board with using cookies and monitoring user behaviour across their apps and sites. It’s this kind of data that informs more intelligent targeting, but in truth, this only provides a mere snapshot of what your users are actually up to day to day. What about the time they spend outside of your app, your site, or the sites you have cookies on?

The technology behind cookie tracking is outdated and slowly dying, there’s no denying it. Cookies only allow access to data from sites where they are in place, which represent fewer and fewer sites every day. Besides, the emergence of new cookie anti-tracking features such as Apple’s latest iOS 11 new feature for Safari mean their days are numbered more than ever before.

The technology behind cookie tracking is outdated and slowly dying.

Considering people currently use their mobile devices over four hours a day on average, to truly understand users you need data that represents all activity across all apps and websites, giving marketers a complete picture of users’ behavior.

3.Time to get real-time

Everyone has a favourite piece of historical data, a trick card up their sleeve to sway an argument or press home a decision. However, relying on those ‘old faithfuls’ is being made redundant by the ever more rapid progress made in interpreting data in real-time.

A recent McKinsey global executive survey found that higher-performing companies are up to five times more likely to work with real-time and unstructured data compared to lower-performing businesses. Activating your users’ data in real time is no longer a nice-to-have, it’s very much the here and now.

Eliminate inefficiency and misplaced spend by making sure the data you rely upon is as current and relevant as possible.

4.Leave extrapolation to the chemists

Extrapolating data once it has been acquired has its advantages. Given that trends can be replicated, updated, and tweaked when necessary, it’s particularly beneficial when data is in shorter supply than what would be considered ideal.

But all the projection in the world isn’t going to help you spot turning points in consumer tastes and behaviour ahead of time; an effect that becomes more acute the further out you forecast. Extrapolated data sets are becoming less useful as the advances in real-time data take hold.

Genuine user-based data will quickly become the norm in planning ad strategy, so cut down on inefficiencies by making sure your data sets are from genuine users and not projected based on a much smaller sample set.

5. Recommendations over ads

Like it or not, ‘advertising’ is still something of a dirty word to your average consumer. By adopting a more consumer-centric approach and giving them the control to share their data or not, combined with the best targeting tech available, brands have the opportunity to position their messages as personalised recommendations rather than simple promo ads. This is an ideal that can only be achieved through more accurate, transparent, opt-in collected data.

With the European Union’s GDPR coming into force officially in May next year, it’s more than just an ideal - it’s set to become the base level requirement for anyone hoping to do business on the continent.

These factors should be front of mind for any marketer aiming to be in a position to employ a future-proof, cutting edge data strategy fit for targeting any niche of consumer.

About Christophe Bize

Christophe Bize is VP of Data and Mobile Analytics at Ogury. He started his career in the mobile industry in 1998 and spent 12 years at Orange working across several affiliates before moving to Blackberry where he took a number of different positions including Enterprise Sales Manager, Key Account Director and Commercial Director. He later headed to Comscore France, where he spent three years as a Senior Sales Director.

Christophe’s background spans the manufacturing side and the carrier side of the mobile industry. Having built a career looking at mobile data from all sides - hardware, carrier and industry analytics - Christophe has a strong view on digital - particularly across ad verification, analytics and audiences. He moved to Ogury as he was highly motivated by the accuracy and volume of the data the platform collects on mobile.